计算机视觉是由许多数据集驱动的，这些数据集可用于培训或评估新方法。但是，每个数据集都有不同的类标签，类的视觉定义，遵循特定分布的图像，注释协议等。在本文中，我们探讨了跨数据集之间的视觉语义关系的自动发现。我们想了解数据集中某个类的实例与另一个数据集中另一类的实例有关。他们是否处于身份，父母/孩子的重叠关系中？还是他们之间没有链接？为了找到跨数据集的标签之间的关系，我们根据语言，视觉和两者的组合提出方法。我们的方法可以有效地发现跨数据集和关系类型的标签关系。我们使用这些结果进行更深入的检查，以了解为什么实例相关，找到班级缺失方面，并利用我们的关系来创建更细粒度的注释。我们得出的结论是，不能通过单独查看类的名称来建立标签关系，因为它们在很大程度上取决于每个数据集的构建方式。

我们解决了转移学习中的集合选择问题：给出了大量的源模型，我们要选择一个模型的集合，在对目标训练集的微调后，在目标测试集上产生最佳性能。由于微调所有可能的合奏是计算禁止的，因此我们目的是使用计算上有效的可转换度量来预测目标数据集的性能。我们提出了用于此任务的几个新的可转换性指标，并在对语义细分的具有挑战性和现实的转移学习设置中进行评估：我们通过考虑涵盖各种图像域的各种数据集来创建一个大型和多样化的源模型池，两种不同架构和两个预训练计划。鉴于此池，我们自动选择子集，以在给定的目标数据集上形成良好的集合。我们将通过我们的方法选择的合奏与两个基线进行比较，该基线选择单个源模型，其中（1）与我们的方法相同;或（2）从包含大源模型的池，每个池具有与集合相似的容量。平均超过17个目标数据集，我们分别以6.0％和2.5％的相对平均值越优于这些基线。

转移学习已成为利用计算机视觉中预先训练模型的流行方法。然而，在不执行计算上昂贵的微调的情况下，难以量化哪个预先训练的源模型适用于特定目标任务，或者相反地，可以容易地适应预先训练的源模型的任务。在这项工作中，我们提出了高斯Bhattacharyya系数（GBC），一种用于量化源模型和目标数据集之间的可转换性的新方法。在第一步中，我们在由源模型定义的特征空间中嵌入所有目标图像，并表示使用每类高斯。然后，我们使用Bhattacharyya系数估计它们的成对类可分离性，从而产生了一种简单有效的源模型转移到目标任务的程度。我们在数据集和架构选择的上下文中评估GBC在图像分类任务上。此外，我们还对更复杂的语义分割转移性估算任务进行实验。我们证明GBC在语义分割设置中大多数评估标准上的最先进的可转移性度量，匹配图像分类中的数据集转移性的最高方法的性能，并且在图像分类中执行最佳的架构选择问题。

转移学习可以在源任务上重新使用知识来帮助学习目标任务。一种简单的转移学习形式在当前的最先进的计算机视觉模型中是常见的，即预先训练ILSVRC数据集上的图像分类模型，然后在任何目标任务上进行微调。然而，先前对转移学习的系统研究已经有限，并且预计工作的情况并不完全明白。在本文中，我们对跨越不同的图像域进行了广泛的转移学习实验探索（消费者照片，自主驾驶，空中图像，水下，室内场景，合成，特写镜头）和任务类型（语义分割，物体检测，深度估计，关键点检测）。重要的是，这些都是与现代计算机视觉应用相关的复杂的结构化的输出任务类型。总共执行超过2000年的转移学习实验，包括许多来源和目标来自不同的图像域，任务类型或两者。我们系统地分析了这些实验，了解图像域，任务类型和数据集大小对传输学习性能的影响。我们的研究导致了几个见解和具体建议：（1）对于大多数任务，存在一个显着优于ILSVRC'12预培训的来源; （2）图像领域是实现阳性转移的最重要因素; （3）源数据集应该\ \ emph {include}目标数据集的图像域以获得最佳结果; （4）与此同时，当源任务的图像域比目标的图像域时，我们只观察小的负面影响; （5）跨任务类型的转移可能是有益的，但其成功严重依赖于源和目标任务类型。

在在下游决策取决于预测概率的安全关键应用中，校准神经网络是最重要的。测量校准误差相当于比较两个实证分布。在这项工作中，我们引入了由经典Kolmogorov-Smirnov（KS）统计测试的自由校准措施，其中主要思想是比较各自的累积概率分布。由此，通过通过Quidsime使用可微分函数来近似经验累积分布，我们获得重新校准函数，将网络输出映射到实际（校准的）类分配概率。使用停滞校准组进行脊柱拟合，并在看不见的测试集上评估所获得的重新校准功能。我们测试了我们对各种图像分类数据集的现有校准方法的方法，并且我们的样条键的重新校准方法始终如一地优于KS错误的现有方法以及其他常用的校准措施。我们的代码可在https://github.com/kartikgupta-at-anu/spline-calibration获得。

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/ fungraph/neural_catacaustics/

Edge computing is changing the face of many industries and services. Common edge computing models offload computing which is prone to security risks and privacy violation. However, advances in deep learning enabled Internet of Things (IoTs) to take decisions and run cognitive tasks locally. This research introduces a decentralized-control edge model where most computation and decisions are moved to the IoT level. The model aims at decreasing communication to the edge which in return enhances efficiency and decreases latency. The model also avoids data transfer which raises security and privacy risks. To examine the model, we developed SAFEMYRIDES, a scene-aware ridesharing monitoring system where smart phones are detecting violations at the runtime. Current real-time monitoring systems are costly and require continuous network connectivity. The system uses optimized deep learning that run locally on IoTs to detect violations in ridesharing and record violation incidences. The system would enhance safety and security in ridesharing without violating privacy.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

The application of deep learning algorithms to financial data is difficult due to heavy non-stationarities which can lead to over-fitted models that underperform under regime changes. Using the Numerai tournament data set as a motivating example, we propose a machine learning pipeline for trading market-neutral stock portfolios based on tabular data which is robust under changes in market conditions. We evaluate various machine-learning models, including Gradient Boosting Decision Trees (GBDTs) and Neural Networks with and without simple feature engineering, as the building blocks for the pipeline. We find that GBDT models with dropout display high performance, robustness and generalisability with relatively low complexity and reduced computational cost. We then show that online learning techniques can be used in post-prediction processing to enhance the results. In particular, dynamic feature neutralisation, an efficient procedure that requires no retraining of models and can be applied post-prediction to any machine learning model, improves robustness by reducing drawdown in volatile market conditions. Furthermore, we demonstrate that the creation of model ensembles through dynamic model selection based on recent model performance leads to improved performance over baseline by improving the Sharpe and Calmar ratios. We also evaluate the robustness of our pipeline across different data splits and random seeds with good reproducibility of results.